Merge pull request #24 from ttimbers/python-ml

Added workshop description, workshop instructor bios and headshots

workshops/ml_python.qmd

@@ -1,35 +1,47 @@
 ---
-title: ML Python (Details TBD)
+title: Introduction to machine learning in Python with Scikit-learn
 author:
-  - name: Instructor 1 name
+  - name: Tiffany Timbers
     affiliations:
-      - name: Instructor 1 affiliation
-  - name: Instructor 2 name (remove if single instructor)
+      - name: Department of Statistics, University of British Columbia
+  - name: Trevor Campbell
     affiliations:
-      - name: Instructor 2 affiliation      
+      - name: Department of Statistics, University of British Columbia
 description: |
-  1-sentence summary of workshop.
-categories: [add, comma, separated, categories]
+  Machine learning with tabular data using Python's the Scikit-learn framework.
+categories: [Python, Scikit-learn, machine learning, modeling]
 ---
 
 # Description
 
-Full workshop description goes here. Multi-paragraph ok.
+This workshop will teach you how to perform machine learning for prediction 
+in Python using the widely-used Scikit-learn learn package.
+You will be introduced to best practices for machine learning model creation 
+and selection, including data splitting, pre-processing, 
+parameter and model optimization,
+as well as results visualization and communication.
+Workshop examples will begin with simple, intuitive models 
+(e.g., K-nearest neighbors, linear regression)
+but also demonstrate the use of more commonly used and industry standard models 
+(e.g., L1 Regularized regression and Light Gradient Boosting Machines).
+The workshop will focus on demonstrating how to do this 
+using the modern Scikit-learn pipeline syntax.
 
 # Audience
 
 This course is for you if you:
 
--   list at least
+-   are comfortable using Python and the pandas and package to read, transform and reshape data
 
--   three attributes
+-   have experience making a variety of graphs with any Python package.
 
--   for your target audience
+Intermediate or expert familiarity with modeling or machine learning is not required.
 
 # Instructor(s)
 
 |                               |     |                                             |
-|------------------|------------------|------------------------------------|
-| ![](images/name-lastname.jpg) |     | Instructor bio, including link to homepage. |
+|-------------------|-------------------|------------------------------------|
+| ![](images/tiffany-timbers.jpg) |     | [Tiffany Timbers](https://www.tiffanytimbers.com/) is an Associate Professor of Teaching in the Department of Statistics and Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia. In these roles she teaches and develops curriculum around the responsible application of Data Science to solve real-world problems. One of her favorite courses she teaches is a graduate course on collaborative software development, which focuses on teaching how to create R and Python packages using modern tools and workflows. She is an author of [Data Science: A First Introduction](https://python.datasciencebook.ca/) - a textbook that serves as an approachable introduction to the world of data science, written now in both [R](https://python.datasciencebook.ca/) and [Python](https://datasciencebook.ca/).  |
+| ![](images/trevor-campbell.jpg) |     | [Trevor Campbell](https://trevorcampbell.me/) is an Associate Professor in the Department of Statistics at the University of British Columbia. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and Bayesian theory. He was previously a postdoctoral associate advised by Tamara Broderick in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D. candidate under Jonathan How in the Laboratory for Information and Decision Systems (LIDS) at MIT, and before that he was in the Engineering Science program at the University of Toronto. He is an author of [Data Science: A First Introduction](https://python.datasciencebook.ca/) - a textbook that serves as an approachable introduction to the world of data science, written now in both [R](https://python.datasciencebook.ca/) and [Python](https://datasciencebook.ca/).  |
 
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